Aimed at narrowing the domain gap, a Domain Alignment Module (DAM) is proposed and illustrated in Fig. REF . For a poster \({x_{pst}}\)  in training, we first get a masked poster \({x^{mask}_{pst}}\)  guided by its layout \({l_{GT}}\)  and utilize a pretrained inpainting Net [1]} (InpNet) to obtain repaired result \({x^{inp}_{pst}}\) . Besides, we use a pretrained salience detection net (SalNet) [2]} to point out subject locations and help subsequent networks better understand image compositions. So \({x^{inp}_{pst}}\)  is sent into SalNet to output \({x^{sal}_{pst}}\)  and these two are concatenated as \({\hat{x}_{pst}}\) . While in test, an image \({x_{img}}\)  is just sent into SalNet to get \({x^{sal}_{img}}\)  and concatenated with it to form \({\hat{x}_{img}}\) . Through inpainting and salience detection, the marks of graphic elements are almost eliminated and the domain gap between \({\hat{x}_{pst}}\)  and \({\hat{x}_{img}}\)  is much smaller than that between \({x_{pst}}\)  and \({x_{img}}\) .
